The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
In agriculture, farmers need to balance the competing goals of supplying sufficient fertilizer for their crops while minimizing the cost and impact to the environment. Testing crops for nitrogen has historically involved collecting samples from plants within one or more plots of land and testing the samples using a spectrometer to measure the concentration of nitrogen found within the samples. However, the aforementioned testing methodology can take a significant amount of time since the samples would often need be collected manually for submission to a remote or on-site lab.
In past decades, researches have demonstrated that remote sensing techniques have the potential to estimate nitrogen content for different plants. Since remote sensing imagery can be taken for a large area of land, such as through use of satellites or aircrafts, remote sensing imagery can be far more convenient and efficient to collect than actual plant samples. Different remote sensing techniques have been developed for estimating variables that are related to the biophysical, physiological, or biochemical characteristics of plants.
According to the spectral resolutions of deployed sensors, imagery can either be hyperspectral or multispectral. The main difference between hyperspectral and multispectral imagery is the number of bands that can be detected, with hyperspectral images typically including 128 bands compared to 5-7 bands for multispectral images. In general, hyperspectral imagery is more powerful for selection of narrow bands which are sensitive to specific crop variables, such as nitrogen content. Extensive research has been done in the past to apply hyperspectral imagery to increase the sensitivity of the vegetation indices to chlorophyll, nitrogen, and other plant minerals. Such investigations have mainly been performed on leaf level or on canopies grown under controlled conditions. For example, one study has shown that leaf nitrogen content could be quickly estimated by measuring leaf reflectance at 550 nm. There are also a limited number of results on band selections where hyperspectral images were acquired under natural conditions. In addition, hyperspectral remote sensing images have been shown to have a lot of potential in estimating moisture and certain minerals in the soil. However, existing methods use either single band or certain vegetation indexes to estimate plant nitrogen content, rather than exploring the full potential of hyperspectral and multispectral imagery.